Efficient inference for expressive comparative preference languages
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The Association for the Advancement of Artificial Intelligence (AAAI), The International Joint Conferences on Artificial Intelligence, Inc. (IJCAI)
A fundamental task for reasoning with preferences is the following: given input preference information from a user, and outcomes α and β, should we infer that the user will prefer α to β? For CP-nets and related comparative preference formalisms, inferring a preference of α over β using the standard definition of derived preference appears to be extremely hard, and has been proved to be PSPACE-complete in general for CP-nets. Such inference is also rather conservative, only making the assumption of transitivity. This paper defines a less conservative approach to inference which can be applied for very general forms of input. It is shown to be efficient for expressive comparative preference languages, allowing comparisons between arbitrary partial tuples (including complete assignments), and with the preferences being ceteris paribus or not.
CP-nets , Constraints , AI , Artificial intelligence
Wilson, N.(2009) 'Efficient Inference for Expressive Comparative Preference Languages', IJCAI-09: Proceedings of the 21st International Joint conference on Artificial intelligence, Pasadena, California, USA, 11-17 July, pp. 961-966.
© 2009 IJCAI; AAAI